Overview

Dataset statistics

Number of variables18
Number of observations12517
Missing cells8
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 MiB
Average record size in memory144.0 B

Variable types

Numeric16
Categorical2

Alerts

rerun_ID has constant value ""Constant
obj_ID is highly overall correlated with run_IDHigh correlation
u is highly overall correlated with g and 2 other fieldsHigh correlation
g is highly overall correlated with u and 6 other fieldsHigh correlation
r is highly overall correlated with u and 7 other fieldsHigh correlation
i is highly overall correlated with u and 7 other fieldsHigh correlation
z is highly overall correlated with g and 6 other fieldsHigh correlation
run_ID is highly overall correlated with obj_IDHigh correlation
spec_obj_ID is highly overall correlated with g and 5 other fieldsHigh correlation
redshift is highly overall correlated with r and 3 other fieldsHigh correlation
plate is highly overall correlated with g and 5 other fieldsHigh correlation
MJD is highly overall correlated with g and 5 other fieldsHigh correlation
class is highly overall correlated with redshiftHigh correlation
alpha has unique valuesUnique
delta has unique valuesUnique

Reproduction

Analysis started2023-06-04 12:25:09.492376
Analysis finished2023-06-04 12:26:31.312386
Duration1 minute and 21.82 seconds
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

obj_ID
Real number (ℝ)

Distinct10592
Distinct (%)84.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.237666 × 1018
Minimum1.2376459 × 1018
Maximum1.2376805 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size97.9 KiB
2023-06-04T12:26:31.499602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.2376459 × 1018
5-th percentile1.2376518 × 1018
Q11.237661 × 1018
median1.2376648 × 1018
Q31.2376711 × 1018
95-th percentile1.2376803 × 1018
Maximum1.2376805 × 1018
Range3.4585772 × 1013
Interquartile range (IQR)1.0106591 × 1013

Descriptive statistics

Standard deviation8.4275987 × 1012
Coefficient of variation (CV)6.8092677 × 10-6
Kurtosis-0.76084707
Mean1.237666 × 1018
Median Absolute Deviation (MAD)5.4916546 × 1012
Skewness0.27699644
Sum1.5491865 × 1022
Variance7.1024421 × 1025
MonotonicityNot monotonic
2023-06-04T12:26:31.805871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.237678803 × 10187
 
0.1%
1.237662302 × 10187
 
0.1%
1.237678858 × 10187
 
0.1%
1.237663785 × 10187
 
0.1%
1.237663783 × 10186
 
< 0.1%
1.237663783 × 10186
 
< 0.1%
1.237654391 × 10186
 
< 0.1%
1.237666302 × 10186
 
< 0.1%
1.237663459 × 10186
 
< 0.1%
1.237663463 × 10186
 
< 0.1%
Other values (10582) 12453
99.5%
ValueCountFrequency (%)
1.237645944 × 10181
< 0.1%
1.237645944 × 10181
< 0.1%
1.237645944 × 10181
< 0.1%
1.237645944 × 10181
< 0.1%
1.237645944 × 10181
< 0.1%
1.237645944 × 10181
< 0.1%
1.237646382 × 10181
< 0.1%
1.237646382 × 10181
< 0.1%
1.237646792 × 10181
< 0.1%
1.237646794 × 10181
< 0.1%
ValueCountFrequency (%)
1.23768053 × 10181
< 0.1%
1.23768053 × 10181
< 0.1%
1.23768053 × 10181
< 0.1%
1.23768053 × 10181
< 0.1%
1.23768053 × 10181
< 0.1%
1.23768053 × 10181
< 0.1%
1.23768053 × 10181
< 0.1%
1.23768053 × 10181
< 0.1%
1.23768053 × 10181
< 0.1%
1.23768053 × 10181
< 0.1%

alpha
Real number (ℝ)

Distinct12517
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean175.5333
Minimum0.0055278279
Maximum359.97429
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size97.9 KiB
2023-06-04T12:26:32.108672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.0055278279
5-th percentile7.6206176
Q1119.45408
median174.94648
Q3239.2597
95-th percentile350.35136
Maximum359.97429
Range359.96876
Interquartile range (IQR)119.80563

Descriptive statistics

Standard deviation104.50373
Coefficient of variation (CV)0.59534988
Kurtosis-0.81995498
Mean175.5333
Median Absolute Deviation (MAD)60.18119
Skewness0.015735684
Sum2197150.3
Variance10921.03
MonotonicityNot monotonic
2023-06-04T12:26:32.413075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
135.6891066 1
 
< 0.1%
126.2736401 1
 
< 0.1%
117.8356583 1
 
< 0.1%
339.4422338 1
 
< 0.1%
342.9652278 1
 
< 0.1%
115.1640613 1
 
< 0.1%
248.2678019 1
 
< 0.1%
225.8383929 1
 
< 0.1%
153.4098326 1
 
< 0.1%
157.5426717 1
 
< 0.1%
Other values (12507) 12507
99.9%
ValueCountFrequency (%)
0.005527827924 1
< 0.1%
0.01333666183 1
< 0.1%
0.02425788497 1
< 0.1%
0.05101121243 1
< 0.1%
0.07573854975 1
< 0.1%
0.08524942209 1
< 0.1%
0.0935691169 1
< 0.1%
0.10189313 1
< 0.1%
0.1063297907 1
< 0.1%
0.1072433545 1
< 0.1%
ValueCountFrequency (%)
359.9742918 1
< 0.1%
359.9463159 1
< 0.1%
359.9398061 1
< 0.1%
359.9187627 1
< 0.1%
359.9108226 1
< 0.1%
359.8960375 1
< 0.1%
359.8894143 1
< 0.1%
359.8497258 1
< 0.1%
359.8430004 1
< 0.1%
359.8372293 1
< 0.1%

delta
Real number (ℝ)

Distinct12517
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.589038
Minimum-13.624327
Maximum82.764421
Zeros0
Zeros (%)0.0%
Negative1353
Negative (%)10.8%
Memory size97.9 KiB
2023-06-04T12:26:32.715072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-13.624327
5-th percentile-1.4925307
Q13.9259488
median20.513758
Q337.26374
95-th percentile55.293195
Maximum82.764421
Range96.388748
Interquartile range (IQR)33.337791

Descriptive statistics

Standard deviation18.847095
Coefficient of variation (CV)0.83434697
Kurtosis-0.9394861
Mean22.589038
Median Absolute Deviation (MAD)16.662008
Skewness0.31716581
Sum282746.99
Variance355.213
MonotonicityNot monotonic
2023-06-04T12:26:33.002931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32.49463184 1
 
< 0.1%
45.53364552 1
 
< 0.1%
17.72492137 1
 
< 0.1%
1.096230054 1
 
< 0.1%
1.148783474 1
 
< 0.1%
41.23496726 1
 
< 0.1%
33.0921362 1
 
< 0.1%
36.94484135 1
 
< 0.1%
17.32546465 1
 
< 0.1%
17.91526358 1
 
< 0.1%
Other values (12507) 12507
99.9%
ValueCountFrequency (%)
-13.6243266 1
< 0.1%
-12.68517141 1
< 0.1%
-12.41489591 1
< 0.1%
-12.35314572 1
< 0.1%
-12.02798014 1
< 0.1%
-11.80383484 1
< 0.1%
-11.40214597 1
< 0.1%
-11.2663831 1
< 0.1%
-10.77990673 1
< 0.1%
-10.60182346 1
< 0.1%
ValueCountFrequency (%)
82.76442096 1
< 0.1%
82.28865698 1
< 0.1%
79.25912069 1
< 0.1%
79.17043583 1
< 0.1%
79.13027385 1
< 0.1%
78.69718864 1
< 0.1%
78.3167919 1
< 0.1%
77.76046377 1
< 0.1%
77.69637009 1
< 0.1%
77.36060713 1
< 0.1%

u
Real number (ℝ)

Distinct12396
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.082509
Minimum12.2624
Maximum29.19901
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size97.9 KiB
2023-06-04T12:26:33.295619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum12.2624
5-th percentile18.500686
Q120.39184
median22.14223
Q323.66948
95-th percentile25.792438
Maximum29.19901
Range16.93661
Interquartile range (IQR)3.27764

Descriptive statistics

Standard deviation2.2357948
Coefficient of variation (CV)0.10124732
Kurtosis-0.48531881
Mean22.082509
Median Absolute Deviation (MAD)1.64533
Skewness-0.047261655
Sum276406.76
Variance4.9987785
MonotonicityNot monotonic
2023-06-04T12:26:33.576696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.63465 14
 
0.1%
24.63466 6
 
< 0.1%
24.63468 4
 
< 0.1%
24.63471 2
 
< 0.1%
22.55928 2
 
< 0.1%
23.03799 2
 
< 0.1%
20.03177 2
 
< 0.1%
23.03029 2
 
< 0.1%
20.17831 2
 
< 0.1%
19.70649 2
 
< 0.1%
Other values (12386) 12479
99.7%
ValueCountFrequency (%)
12.2624 1
< 0.1%
14.50678 1
< 0.1%
15.35842 1
< 0.1%
15.38325 1
< 0.1%
15.4468 1
< 0.1%
15.47245 1
< 0.1%
15.58205 1
< 0.1%
15.61601 1
< 0.1%
15.61909 1
< 0.1%
15.62139 1
< 0.1%
ValueCountFrequency (%)
29.19901 1
< 0.1%
28.90174 1
< 0.1%
27.80917 1
< 0.1%
27.8003 1
< 0.1%
27.69898 1
< 0.1%
27.63276 1
< 0.1%
27.55347 1
< 0.1%
27.53008 1
< 0.1%
27.4897 1
< 0.1%
27.4588 1
< 0.1%

g
Real number (ℝ)

Distinct12408
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.634716
Minimum10.51139
Maximum28.9032
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size97.9 KiB
2023-06-04T12:26:33.887398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10.51139
5-th percentile17.130086
Q119.04204
median21.05874
Q322.10094
95-th percentile23.408376
Maximum28.9032
Range18.39181
Interquartile range (IQR)3.0589

Descriptive statistics

Standard deviation2.0135861
Coefficient of variation (CV)0.097582445
Kurtosis-0.27669028
Mean20.634716
Median Absolute Deviation (MAD)1.31724
Skewness-0.42133744
Sum258284.74
Variance4.0545288
MonotonicityNot monotonic
2023-06-04T12:26:34.170360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.14258 3
 
< 0.1%
22.2753 2
 
< 0.1%
20.77017 2
 
< 0.1%
18.20431 2
 
< 0.1%
21.45683 2
 
< 0.1%
20.60398 2
 
< 0.1%
18.62643 2
 
< 0.1%
21.67759 2
 
< 0.1%
21.23085 2
 
< 0.1%
17.99198 2
 
< 0.1%
Other values (12398) 12496
99.8%
ValueCountFrequency (%)
10.51139 1
< 0.1%
12.67902 1
< 0.1%
13.73052 1
< 0.1%
13.8347 1
< 0.1%
13.88656 1
< 0.1%
14.02731 1
< 0.1%
14.1092 1
< 0.1%
14.11843 1
< 0.1%
14.30195 1
< 0.1%
14.37224 1
< 0.1%
ValueCountFrequency (%)
28.9032 1
< 0.1%
27.26466 1
< 0.1%
27.15748 1
< 0.1%
27.07299 1
< 0.1%
26.88175 1
< 0.1%
26.77787 1
< 0.1%
26.53025 1
< 0.1%
26.44887 1
< 0.1%
26.39754 1
< 0.1%
26.3144 1
< 0.1%

r
Real number (ℝ)

Distinct12376
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.666776
Minimum10.06854
Maximum27.39709
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size97.9 KiB
2023-06-04T12:26:34.630947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10.06854
5-th percentile16.428314
Q118.21046
median20.13639
Q321.05019
95-th percentile22.087562
Maximum27.39709
Range17.32855
Interquartile range (IQR)2.83973

Descriptive statistics

Standard deviation1.8415992
Coefficient of variation (CV)0.093640117
Kurtosis-0.33040625
Mean19.666776
Median Absolute Deviation (MAD)1.22932
Skewness-0.51659383
Sum246169.03
Variance3.3914876
MonotonicityNot monotonic
2023-06-04T12:26:35.117912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.95005 2
 
< 0.1%
17.32595 2
 
< 0.1%
20.08151 2
 
< 0.1%
18.19844 2
 
< 0.1%
17.62665 2
 
< 0.1%
19.76283 2
 
< 0.1%
20.6039 2
 
< 0.1%
20.10729 2
 
< 0.1%
20.72109 2
 
< 0.1%
22.47901 2
 
< 0.1%
Other values (12366) 12497
99.8%
ValueCountFrequency (%)
10.06854 1
< 0.1%
11.74664 1
< 0.1%
12.80579 1
< 0.1%
12.86339 1
< 0.1%
13.06969 1
< 0.1%
13.20467 1
< 0.1%
13.20538 1
< 0.1%
13.40388 1
< 0.1%
13.45793 1
< 0.1%
13.61539 1
< 0.1%
ValueCountFrequency (%)
27.39709 1
< 0.1%
27.33476 1
< 0.1%
25.22189 1
< 0.1%
24.92102 1
< 0.1%
24.81002 1
< 0.1%
24.8026 1
< 0.1%
24.80203 2
< 0.1%
24.80202 2
< 0.1%
24.80159 1
< 0.1%
24.76983 1
< 0.1%

i
Real number (ℝ)

Distinct12384
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.118716
Minimum11.29956
Maximum30.1546
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size97.9 KiB
2023-06-04T12:26:35.663416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum11.29956
5-th percentile16.05422
Q117.79078
median19.44152
Q320.44477
95-th percentile21.62574
Maximum30.1546
Range18.85504
Interquartile range (IQR)2.65399

Descriptive statistics

Standard deviation1.7555926
Coefficient of variation (CV)0.091825862
Kurtosis-0.25412527
Mean19.118716
Median Absolute Deviation (MAD)1.20771
Skewness-0.42513524
Sum239308.97
Variance3.0821052
MonotonicityNot monotonic
2023-06-04T12:26:36.194637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.04249 3
 
< 0.1%
20.7219 3
 
< 0.1%
18.51546 3
 
< 0.1%
19.16573 2
 
< 0.1%
18.94915 2
 
< 0.1%
19.01537 2
 
< 0.1%
18.99609 2
 
< 0.1%
17.23952 2
 
< 0.1%
19.70479 2
 
< 0.1%
17.83562 2
 
< 0.1%
Other values (12374) 12494
99.8%
ValueCountFrequency (%)
11.29956 1
< 0.1%
12.22783 1
< 0.1%
12.41727 1
< 0.1%
12.68676 1
< 0.1%
12.73416 1
< 0.1%
12.80524 1
< 0.1%
13.00044 1
< 0.1%
13.03615 1
< 0.1%
13.10743 1
< 0.1%
13.21151 1
< 0.1%
ValueCountFrequency (%)
30.1546 1
< 0.1%
24.78471 1
< 0.1%
24.6315 1
< 0.1%
24.47336 1
< 0.1%
24.36193 1
< 0.1%
24.36182 1
< 0.1%
24.36181 1
< 0.1%
24.3618 1
< 0.1%
23.80095 1
< 0.1%
23.69384 1
< 0.1%

z
Real number (ℝ)

Distinct12383
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.808766
Minimum10.22551
Maximum26.42779
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size97.9 KiB
2023-06-04T12:26:36.730631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10.22551
5-th percentile15.781204
Q117.51829
median19.04271
Q319.96365
95-th percentile21.496052
Maximum26.42779
Range16.20228
Interquartile range (IQR)2.44536

Descriptive statistics

Standard deviation1.7743137
Coefficient of variation (CV)0.094334398
Kurtosis-0.19453224
Mean18.808766
Median Absolute Deviation (MAD)1.18575
Skewness-0.26934744
Sum235429.33
Variance3.1481889
MonotonicityNot monotonic
2023-06-04T12:26:37.255538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.8269 14
 
0.1%
19.8066 3
 
< 0.1%
19.6901 2
 
< 0.1%
19.55999 2
 
< 0.1%
16.03147 2
 
< 0.1%
19.26989 2
 
< 0.1%
17.97244 2
 
< 0.1%
16.90271 2
 
< 0.1%
17.84232 2
 
< 0.1%
19.53197 2
 
< 0.1%
Other values (12373) 12484
99.7%
ValueCountFrequency (%)
10.22551 1
< 0.1%
10.91847 1
< 0.1%
11.78913 1
< 0.1%
12.1256 1
< 0.1%
12.39726 1
< 0.1%
12.40214 1
< 0.1%
12.42432 1
< 0.1%
12.51869 1
< 0.1%
12.69585 1
< 0.1%
12.71279 1
< 0.1%
ValueCountFrequency (%)
26.42779 1
< 0.1%
25.33364 1
< 0.1%
24.78273 1
< 0.1%
24.39331 1
< 0.1%
24.24891 1
< 0.1%
24.21933 1
< 0.1%
24.00087 1
< 0.1%
23.99034 1
< 0.1%
23.81899 1
< 0.1%
23.75511 1
< 0.1%

run_ID
Real number (ℝ)

Distinct247
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4767.6645
Minimum109
Maximum8162
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size97.9 KiB
2023-06-04T12:26:37.799760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum109
5-th percentile1462
Q13606
median4504
Q35959
95-th percentile8102
Maximum8162
Range8053
Interquartile range (IQR)2353

Descriptive statistics

Standard deviation1962.2261
Coefficient of variation (CV)0.41156968
Kurtosis-0.76082797
Mean4767.6645
Median Absolute Deviation (MAD)1279
Skewness0.27701437
Sum59676856
Variance3850331.5
MonotonicityNot monotonic
2023-06-04T12:26:38.331522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4263 591
 
4.7%
7773 517
 
4.1%
5935 354
 
2.8%
4649 327
 
2.6%
3699 323
 
2.6%
7712 306
 
2.4%
8103 278
 
2.2%
4849 278
 
2.2%
6573 260
 
2.1%
3927 213
 
1.7%
Other values (237) 9070
72.5%
ValueCountFrequency (%)
109 6
 
< 0.1%
211 2
 
< 0.1%
307 2
 
< 0.1%
308 8
 
0.1%
745 9
 
0.1%
752 23
 
0.2%
756 20
 
0.2%
1140 58
0.5%
1231 11
 
0.1%
1239 64
0.5%
ValueCountFrequency (%)
8162 10
 
0.1%
8157 20
0.2%
8156 19
0.2%
8155 4
 
< 0.1%
8150 1
 
< 0.1%
8116 41
0.3%
8115 7
 
0.1%
8112 41
0.3%
8111 17
0.1%
8110 9
 
0.1%

rerun_ID
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size97.9 KiB
301
12517 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters37551
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row301
2nd row301
3rd row301
4th row301
5th row301

Common Values

ValueCountFrequency (%)
301 12517
100.0%

Length

2023-06-04T12:26:38.876892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T12:26:39.212314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
301 12517
100.0%

Most occurring characters

ValueCountFrequency (%)
3 12517
33.3%
0 12517
33.3%
1 12517
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 37551
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 12517
33.3%
0 12517
33.3%
1 12517
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 37551
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 12517
33.3%
0 12517
33.3%
1 12517
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37551
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 12517
33.3%
0 12517
33.3%
1 12517
33.3%

cam_col
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean3.5207734
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size97.9 KiB
2023-06-04T12:26:39.373372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.5406491
Coefficient of variation (CV)0.43758825
Kurtosis-1.0922786
Mean3.5207734
Median Absolute Deviation (MAD)1
Skewness-0.029926008
Sum44066
Variance2.3735996
MonotonicityNot monotonic
2023-06-04T12:26:39.586558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 2800
22.4%
2 2542
20.3%
5 2346
18.7%
3 2027
16.2%
6 1434
11.5%
1 1367
10.9%
(Missing) 1
 
< 0.1%
ValueCountFrequency (%)
1 1367
10.9%
2 2542
20.3%
3 2027
16.2%
4 2800
22.4%
5 2346
18.7%
6 1434
11.5%
ValueCountFrequency (%)
6 1434
11.5%
5 2346
18.7%
4 2800
22.4%
3 2027
16.2%
2 2542
20.3%
1 1367
10.9%

field_ID
Real number (ℝ)

Distinct694
Distinct (%)5.5%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean186.15252
Minimum11
Maximum847
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size97.9 KiB
2023-06-04T12:26:39.862211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile32
Q181
median148
Q3239
95-th percentile463
Maximum847
Range836
Interquartile range (IQR)158

Descriptive statistics

Standard deviation150.08428
Coefficient of variation (CV)0.80624357
Kurtosis4.7754972
Mean186.15252
Median Absolute Deviation (MAD)75
Skewness1.9221308
Sum2329885
Variance22525.29
MonotonicityNot monotonic
2023-06-04T12:26:40.140095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69 81
 
0.6%
68 69
 
0.6%
58 68
 
0.5%
61 65
 
0.5%
74 65
 
0.5%
60 64
 
0.5%
57 63
 
0.5%
52 62
 
0.5%
104 62
 
0.5%
65 62
 
0.5%
Other values (684) 11855
94.7%
ValueCountFrequency (%)
11 15
0.1%
12 20
0.2%
13 13
 
0.1%
14 20
0.2%
15 15
0.1%
16 33
0.3%
17 20
0.2%
18 33
0.3%
19 29
0.2%
20 26
0.2%
ValueCountFrequency (%)
847 1
 
< 0.1%
846 2
 
< 0.1%
845 2
 
< 0.1%
844 2
 
< 0.1%
843 3
< 0.1%
842 2
 
< 0.1%
841 1
 
< 0.1%
840 1
 
< 0.1%
839 5
< 0.1%
838 1
 
< 0.1%

spec_obj_ID
Real number (ℝ)

Distinct12516
Distinct (%)100.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5.7795909 × 1018
Minimum2.9957929 × 1017
Maximum1.4126851 × 1019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size97.9 KiB
2023-06-04T12:26:40.409341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2.9957929 × 1017
5-th percentile8.0140073 × 1017
Q12.7528765 × 1018
median5.6611781 × 1018
Q38.3396895 × 1018
95-th percentile1.1555313 × 1019
Maximum1.4126851 × 1019
Range1.3827272 × 1019
Interquartile range (IQR)5.586813 × 1018

Descriptive statistics

Standard deviation3.3143284 × 1018
Coefficient of variation (CV)0.57345381
Kurtosis-0.88008533
Mean5.7795909 × 1018
Median Absolute Deviation (MAD)2.7619093 × 1018
Skewness0.19126475
Sum7.233736 × 1022
Variance1.0984773 × 1037
MonotonicityNot monotonic
2023-06-04T12:26:40.710308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.543777369 × 10181
 
< 0.1%
7.197931812 × 10181
 
< 0.1%
1.249999379 × 10191
 
< 0.1%
1.032458749 × 10191
 
< 0.1%
1.032563588 × 10191
 
< 0.1%
3.559032633 × 10181
 
< 0.1%
1.22882383 × 10191
 
< 0.1%
1.522271697 × 10181
 
< 0.1%
6.003555689 × 10181
 
< 0.1%
6.014773732 × 10181
 
< 0.1%
Other values (12506) 12506
99.9%
ValueCountFrequency (%)
2.995792876 × 10171
< 0.1%
2.995908325 × 10171
< 0.1%
2.995935813 × 10171
< 0.1%
2.99643884 × 10171
< 0.1%
3.007620869 × 10171
< 0.1%
3.175296401 × 10171
< 0.1%
3.334423276 × 10171
< 0.1%
3.344022008 × 10171
< 0.1%
3.344222669 × 10171
< 0.1%
3.344788917 × 10171
< 0.1%
ValueCountFrequency (%)
1.4126851 × 10191
< 0.1%
1.41268411 × 10191
< 0.1%
1.412683808 × 10191
< 0.1%
1.41245937 × 10191
< 0.1%
1.412456319 × 10191
< 0.1%
1.412355439 × 10191
< 0.1%
1.412349199 × 10191
< 0.1%
1.41234065 × 10191
< 0.1%
1.412339771 × 10191
< 0.1%
1.412337572 × 10191
< 0.1%

class
Categorical

Distinct3
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size97.9 KiB
GALAXY
7374 
STAR
2657 
QSO
2485 

Length

Max length6
Median length6
Mean length4.9797859
Min length3

Characters and Unicode

Total characters62327
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGALAXY
2nd rowGALAXY
3rd rowGALAXY
4th rowGALAXY
5th rowGALAXY

Common Values

ValueCountFrequency (%)
GALAXY 7374
58.9%
STAR 2657
 
21.2%
QSO 2485
 
19.9%
(Missing) 1
 
< 0.1%

Length

2023-06-04T12:26:41.065606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-04T12:26:41.333932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
galaxy 7374
58.9%
star 2657
 
21.2%
qso 2485
 
19.9%

Most occurring characters

ValueCountFrequency (%)
A 17405
27.9%
G 7374
11.8%
L 7374
11.8%
X 7374
11.8%
Y 7374
11.8%
S 5142
 
8.3%
T 2657
 
4.3%
R 2657
 
4.3%
Q 2485
 
4.0%
O 2485
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 62327
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 17405
27.9%
G 7374
11.8%
L 7374
11.8%
X 7374
11.8%
Y 7374
11.8%
S 5142
 
8.3%
T 2657
 
4.3%
R 2657
 
4.3%
Q 2485
 
4.0%
O 2485
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 62327
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 17405
27.9%
G 7374
11.8%
L 7374
11.8%
X 7374
11.8%
Y 7374
11.8%
S 5142
 
8.3%
T 2657
 
4.3%
R 2657
 
4.3%
Q 2485
 
4.0%
O 2485
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 62327
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 17405
27.9%
G 7374
11.8%
L 7374
11.8%
X 7374
11.8%
Y 7374
11.8%
S 5142
 
8.3%
T 2657
 
4.3%
R 2657
 
4.3%
Q 2485
 
4.0%
O 2485
 
4.0%

redshift
Real number (ℝ)

Distinct12454
Distinct (%)99.5%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.58501153
Minimum-0.006863183
Maximum7.011245
Zeros57
Zeros (%)0.5%
Negative1726
Negative (%)13.8%
Memory size97.9 KiB
2023-06-04T12:26:41.586684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0.006863183
5-th percentile-0.00030750335
Q10.057747163
median0.39966045
Q30.7147072
95-th percentile2.247079
Maximum7.011245
Range7.0181082
Interquartile range (IQR)0.65696004

Descriptive statistics

Standard deviation0.75014327
Coefficient of variation (CV)1.2822709
Kurtosis9.765086
Mean0.58501153
Median Absolute Deviation (MAD)0.3342527
Skewness2.50049
Sum7322.0043
Variance0.56271493
MonotonicityNot monotonic
2023-06-04T12:26:41.873108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 57
 
0.5%
0.1157107 2
 
< 0.1%
1.064356 2
 
< 0.1%
-0.0001203588 2
 
< 0.1%
0.1320406 2
 
< 0.1%
0.5536857 2
 
< 0.1%
0.5918089 2
 
< 0.1%
0.6347936 1
 
< 0.1%
0.7403097 1
 
< 0.1%
0.4734564 1
 
< 0.1%
Other values (12444) 12444
99.4%
ValueCountFrequency (%)
-0.006863183 1
< 0.1%
-0.004136078 1
< 0.1%
-0.004020127 1
< 0.1%
-0.004016399 1
< 0.1%
-0.003844469 1
< 0.1%
-0.003554939 1
< 0.1%
-0.003324229 1
< 0.1%
-0.003317097 1
< 0.1%
-0.003153456 1
< 0.1%
-0.002675624 1
< 0.1%
ValueCountFrequency (%)
7.011245 1
< 0.1%
7.010263 1
< 0.1%
7.00387 1
< 0.1%
6.983865 1
< 0.1%
6.924236 1
< 0.1%
6.919427 1
< 0.1%
6.708698 1
< 0.1%
6.690471 1
< 0.1%
6.543818 1
< 0.1%
6.504732 1
< 0.1%

plate
Real number (ℝ)

Distinct3558
Distinct (%)28.4%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5133.1984
Minimum266
Maximum12547
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size97.9 KiB
2023-06-04T12:26:42.155062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum266
5-th percentile711.75
Q12445
median5028
Q37407
95-th percentile10263
Maximum12547
Range12281
Interquartile range (IQR)4962

Descriptive statistics

Standard deviation2943.7011
Coefficient of variation (CV)0.57346335
Kurtosis-0.88007765
Mean5133.1984
Median Absolute Deviation (MAD)2453
Skewness0.19127403
Sum64247111
Variance8665376.3
MonotonicityNot monotonic
2023-06-04T12:26:42.447554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6280 26
 
0.2%
6380 25
 
0.2%
8491 24
 
0.2%
8278 22
 
0.2%
6516 22
 
0.2%
7407 22
 
0.2%
6154 21
 
0.2%
5310 21
 
0.2%
9547 21
 
0.2%
7662 21
 
0.2%
Other values (3548) 12291
98.2%
ValueCountFrequency (%)
266 4
< 0.1%
267 1
 
< 0.1%
282 1
 
< 0.1%
296 1
 
< 0.1%
297 3
< 0.1%
298 2
< 0.1%
299 2
< 0.1%
308 1
 
< 0.1%
324 1
 
< 0.1%
326 3
< 0.1%
ValueCountFrequency (%)
12547 3
 
< 0.1%
12545 2
 
< 0.1%
12544 5
< 0.1%
12533 2
 
< 0.1%
12531 12
0.1%
12527 4
 
< 0.1%
12525 7
0.1%
11653 1
 
< 0.1%
11651 4
 
< 0.1%
11649 1
 
< 0.1%

MJD
Real number (ℝ)

Distinct1729
Distinct (%)13.8%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean55584.48
Minimum51608
Maximum58932
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size97.9 KiB
2023-06-04T12:26:42.746358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum51608
5-th percentile52289
Q154180
median55912
Q356781
95-th percentile58199
Maximum58932
Range7324
Interquartile range (IQR)2601

Descriptive statistics

Standard deviation1804.854
Coefficient of variation (CV)0.032470466
Kurtosis-0.74887556
Mean55584.48
Median Absolute Deviation (MAD)1278
Skewness-0.3944991
Sum6.9569535 × 108
Variance3257497.8
MonotonicityNot monotonic
2023-06-04T12:26:43.043983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56269 46
 
0.4%
56340 41
 
0.3%
56187 38
 
0.3%
57358 38
 
0.3%
55831 38
 
0.3%
56570 36
 
0.3%
56214 33
 
0.3%
57488 33
 
0.3%
55481 33
 
0.3%
58462 32
 
0.3%
Other values (1719) 12148
97.1%
ValueCountFrequency (%)
51608 1
 
< 0.1%
51630 4
< 0.1%
51658 1
 
< 0.1%
51662 1
 
< 0.1%
51666 1
 
< 0.1%
51671 7
0.1%
51690 1
 
< 0.1%
51691 2
 
< 0.1%
51692 4
< 0.1%
51693 9
0.1%
ValueCountFrequency (%)
58932 19
0.2%
58931 2
 
< 0.1%
58930 6
 
< 0.1%
58928 8
0.1%
58543 9
0.1%
58526 3
 
< 0.1%
58523 3
 
< 0.1%
58522 13
0.1%
58515 2
 
< 0.1%
58514 10
0.1%

fiber_ID
Real number (ℝ)

Distinct998
Distinct (%)8.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean448.45454
Minimum1
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size97.9 KiB
2023-06-04T12:26:43.344868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile47
Q1225
median437
Q3637
95-th percentile920
Maximum1000
Range999
Interquartile range (IQR)412

Descriptive statistics

Standard deviation267.75301
Coefficient of variation (CV)0.59705721
Kurtosis-0.93998267
Mean448.45454
Median Absolute Deviation (MAD)206
Skewness0.21933647
Sum5612857
Variance71691.676
MonotonicityNot monotonic
2023-06-04T12:26:43.646380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
321 33
 
0.3%
340 29
 
0.2%
458 28
 
0.2%
631 27
 
0.2%
637 27
 
0.2%
457 26
 
0.2%
412 26
 
0.2%
639 26
 
0.2%
584 26
 
0.2%
619 25
 
0.2%
Other values (988) 12243
97.8%
ValueCountFrequency (%)
1 11
0.1%
2 16
0.1%
3 15
0.1%
4 8
0.1%
5 15
0.1%
6 7
0.1%
7 15
0.1%
8 15
0.1%
9 14
0.1%
10 12
0.1%
ValueCountFrequency (%)
1000 8
0.1%
999 11
0.1%
998 2
 
< 0.1%
997 8
0.1%
996 6
< 0.1%
995 3
 
< 0.1%
994 7
0.1%
993 9
0.1%
992 9
0.1%
991 7
0.1%

Interactions

2023-06-04T12:26:25.427482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:25:12.184878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:25:16.438741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:25:22.213184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:25:26.601590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:25:32.360409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:25:38.220928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:25:42.693039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:25:46.815204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:25:52.283690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:25:57.240231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:26:01.197160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:26:05.801219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:26:11.423187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:26:15.472177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:26:19.794528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:26:25.679828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:25:12.484366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:25:16.702126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:25:22.620794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:25:27.250435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:25:32.621903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:25:38.559693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:25:42.950400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:25:47.085672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:25:52.704789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-06-04T12:25:15.918716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:25:21.440340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:25:26.126302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:25:31.847432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:25:37.460082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:25:42.185107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:25:46.294670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:25:51.473416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:25:56.722968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:26:00.703951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:26:05.058067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:26:10.918899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:26:14.975876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:26:19.068347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:26:24.896717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:26:29.170555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:25:16.183448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:25:21.857495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:25:26.371450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:25:32.125348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:25:37.818167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:25:42.446676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:25:46.569702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:25:51.894291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:25:56.993333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:26:00.951113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:26:05.461076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:26:11.177395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:26:15.237149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:26:19.449617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-04T12:26:25.172126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-04T12:26:43.897140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
obj_IDalphadeltaugrizrun_IDcam_colfield_IDspec_obj_IDredshiftplateMJDfiber_IDclass
obj_ID1.0000.022-0.2980.0910.0840.0800.0740.0701.000-0.1080.1460.1780.0520.1780.1770.0160.190
alpha0.0221.0000.1250.0250.0090.0050.001-0.0010.0230.069-0.2190.033-0.0030.0330.0460.0570.149
delta-0.2980.1251.000-0.0170.0090.0230.0280.026-0.2990.026-0.2000.1680.0250.1680.1830.0630.104
u0.0910.025-0.0171.0000.8480.6830.5540.4860.0910.0140.0050.4000.3140.4000.3980.1560.287
g0.0840.0090.0090.8481.0000.9050.7850.7090.0830.016-0.0090.5780.4940.5780.5760.1760.281
r0.0800.0050.0230.6830.9051.0000.9560.9060.080-0.004-0.0360.6720.5540.6720.6690.1770.224
i0.0740.0010.0280.5540.7850.9561.0000.9750.074-0.017-0.0450.6770.5640.6770.6740.1750.280
z0.070-0.0010.0260.4860.7090.9060.9751.0000.069-0.021-0.0470.6550.5490.6540.6500.1710.357
run_ID1.0000.023-0.2990.0910.0830.0800.0740.0691.000-0.1160.1410.1780.0510.1780.1760.0140.190
cam_col-0.1080.0690.0260.0140.016-0.004-0.017-0.021-0.1161.000-0.014-0.060-0.027-0.060-0.0650.1320.071
field_ID0.146-0.219-0.2000.005-0.009-0.036-0.045-0.0470.141-0.0141.000-0.0600.010-0.060-0.068-0.0310.103
spec_obj_ID0.1780.0330.1680.4000.5780.6720.6770.6550.178-0.060-0.0601.0000.4741.0000.9920.2020.316
redshift0.052-0.0030.0250.3140.4940.5540.5640.5490.051-0.0270.0100.4741.0000.4740.4720.1330.585
plate0.1780.0330.1680.4000.5780.6720.6770.6540.178-0.060-0.0601.0000.4741.0000.9920.2020.316
MJD0.1770.0460.1830.3980.5760.6690.6740.6500.176-0.065-0.0680.9920.4720.9921.0000.2030.325
fiber_ID0.0160.0570.0630.1560.1760.1770.1750.1710.0140.132-0.0310.2020.1330.2020.2031.0000.082
class0.1900.1490.1040.2870.2810.2240.2800.3570.1900.0710.1030.3160.5850.3160.3250.0821.000

Missing values

2023-06-04T12:26:29.519153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-04T12:26:30.639786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-06-04T12:26:31.073097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

obj_IDalphadeltaugrizrun_IDrerun_IDcam_colfield_IDspec_obj_IDclassredshiftplateMJDfiber_ID
01.237661e+18135.68910732.49463223.8788222.2753020.3950119.1657318.7937136063012.079.06.543777e+18GALAXY0.6347945812.056354.0171.0
11.237665e+18144.82610131.27418524.7775922.8318822.5844421.1681221.6142745183015.0119.01.176014e+19GALAXY0.77913610445.058158.0427.0
21.237661e+18142.18879035.58244425.2630722.6638920.6097619.3485718.9482736063012.0120.05.152200e+18GALAXY0.6441954576.055592.0299.0
31.237663e+18338.741038-0.40282822.1368223.7765621.6116220.5045419.2501041923013.0214.01.030107e+19GALAXY0.9323469149.058039.0775.0
41.237680e+18345.28259321.18386619.4371817.5802816.4974715.9771115.5446181023013.0137.06.891865e+18GALAXY0.1161236121.056187.0842.0
51.237680e+18340.99512120.58947623.4882723.3377621.3219520.2561519.5454481023013.0110.05.658977e+18QSO1.4246595026.055855.0741.0
61.237679e+1823.23492611.41818821.4697321.1762420.9282920.6082620.4257377733012.0462.01.246262e+19QSO0.58645511069.058456.0113.0
71.237679e+185.43317612.06518622.2497922.0217220.3412619.4879418.8499977733012.0346.06.961443e+18GALAXY0.4770096183.056210.015.0
81.237661e+18200.29047547.19940224.4028622.3566920.6103219.4649018.9585237163015.0108.07.459285e+18GALAXY0.6600126625.056386.0719.0
91.237671e+1839.14969128.10284221.7466920.0349319.1755318.8182318.6542259343014.0122.02.751763e+18STAR-0.0000082444.054082.0232.0
obj_IDalphadeltaugrizrun_IDrerun_IDcam_colfield_IDspec_obj_IDclassredshiftplateMJDfiber_ID
125071.237652e+18167.52749466.92550425.5209021.9039120.3470819.5472219.4262514123014.0124.08.006473e+18GALAXY0.4363717111.056741.0723.0
125081.237659e+18243.54509642.44156623.2271920.9814518.8414018.3105618.0535732253014.0211.01.317310e+18STAR-0.0000941170.052756.024.0
125091.237679e+18319.7634131.15924821.2904719.9905919.6460319.5986319.6468477123012.061.04.719988e+18QSO2.8242254192.055469.0784.0
125101.237662e+18239.93458828.48291922.7114121.3637219.7669918.9424918.4880139193015.0234.05.320102e+18GALAXY0.4629704725.055711.0817.0
125111.237671e+18143.35631015.41385919.4534319.1815619.0990518.8735318.9813059353014.0181.01.079198e+19QSO1.5735569585.057780.0839.0
125121.237665e+18187.10734329.24073117.1693016.1052516.0477116.0912816.1591546493012.038.02.515321e+18STAR0.0004172234.053823.0222.0
125131.237662e+18169.33638341.34971820.1114919.2318618.9654418.8779418.8463638933013.097.03.744904e+18STAR-0.0001473326.054943.0584.0
125141.237662e+18170.43530041.45949722.0494722.1142621.7502221.6744821.1844038933013.0102.09.420409e+18QSO1.2437818367.057429.016.0
125151.237651e+18127.8079091.01342525.2747522.4626220.7271219.5896619.1511212393016.050.05.394425e+18GALAXY0.6258814791.055889.0867.0
125161.237651e+18143.9573941.02513621.9183621.6235920.0269219.1657318.678671239301NaNNaNNaNNaNNaNNaNNaNNaN